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Many different resources are needed for analyzing relevant experimental data in drug design. Currently this data is difficult to access, because it is stored in heterogeneous databases, spread over many platforms, poorly interconnected, incomplete, erroneous, or just not electronically available. In order to establish a high quality database for drug design we have developed a new demand-driven methodology for integrating and semantically enriching heterogeneous data from different researchpmid:7584434 fatcat:qdd5novpvbbv7f5dws2jutz63q